EMG dataset for gesture recognition with arm translation

Myoelectric control has emerged as a promising approach for a wide range
of applications, including controlling limb prosthetics, teleoperating
robots, and enabling immersive interactions in the metaverse. However, the
accuracy and robustness of myoelectric control systems are often affected
by various factors, including muscle fatigue, perspiration, drifts in
electrode positions, and changes in arm position. The latter has received
less attention despite its significant impact on signal quality and
decoding accuracy. To address this gap, we present GREAT, a novel dataset
of surface electromyographic (EMG) signals captured from multiple arm
positions. This dataset, comprising EMG and hand kinematics data from 8
participants performing 6 different hand gestures, provides a
comprehensive resource for investigating position-invariant myoelectric
control decoding algorithms. We envision this dataset to serve as a
valuable resource for both training and benchmarking arm
position-invariant myoelectric control algorithms. Additionally, to
further expand the publicly available data capturing the variability of
EMG signals across diverse arm positions, we propose a novel data
acquisition protocol that can be utilized for future data collection.

肌电控制（Myoelectric control）已成为极具应用前景的技术途径，可广泛应用于肢体假肢控制、机器人遥操作及元宇宙沉浸式交互等诸多场景。然而，肌电控制系统的精度与鲁棒性常受多种因素影响，包括肌肉疲劳、出汗、电极位置偏移以及手臂位置变化。其中，尽管手臂位置变化对信号质量与解码精度存在显著影响，相关研究却相对匮乏。
为填补这一研究空白，我们提出了GREAT数据集——一款采集自多手臂位置的表面肌电（surface electromyographic, EMG）信号数据集。该数据集包含8名受试者完成6种手部动作时采集的肌电与手部运动学数据，可为位置不变肌电控制（position-invariant myoelectric control）解码算法的研究提供系统性的支撑资源。我们期望该数据集可作为训练与基准测试手臂位置不变肌电控制算法的宝贵资源。此外，为进一步扩充覆盖多样手臂位置下肌电信号变异性的公开可用数据集，我们还提出了一种可用于未来数据采集的新型数据采集协议。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于手势识别的表面肌电图数据集，特别关注手臂位置变化对信号的影响。它包含8名参与者执行6种不同手势时在9种手臂位置下采集的EMG信号和手部运动学数据，旨在支持位置不变的肌电控制算法研究。数据以HDF5、CSV和TXT格式提供，包括原始信号、校准后的手指位置信息和试验标签，适用于机器学习或信号处理应用。